class_docstr: The intermediate factors in a Gaussian network can be described
  compactly using a simple parametric representation called the
  canonical form. This representation is closed under the basic
  operations used in inference: factor product, factor division,
  factor reduction, and marginalization. Thus, we define this
  CanonicalDistribution class that allows the inference process to be
  performed on joint Gaussian networks.
  
  A canonical form C (X; K,h,g) is defined as
  
  C (X; K,h,g) = exp( ((-1/2) * X.T * K * X) + (h.T * X) + g)
  
  References
  ----------
  Probabilistic Graphical Models, Principles and Techniques,
  Daphne Koller and Nir Friedman, Section 14.2, Chapter 14.
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code: def __truediv__(self, other):
      return self.divide(other, inplace=False)
  
file_path: pgmpy/pgmpy/factors/distributions/CanonicalDistribution.py
imports: import numpy
  from pgmpy.factors.distributions import BaseDistribution
  from pgmpy.factors.distributions import GaussianDistribution
label_desc: Use image node_id 13 for calling the CanonicalDistribution obj's underlying member method code with example usage: obj.__truediv__(other) and returns: self